Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fc5640dc5f8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fc5640586d8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learn_rate = tf.placeholder(tf.float32)
    return input_real, input_z, learn_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def leaky_relu(x, alpha=0.05, name='leaky_relu'):
    return tf.maximum(x, alpha*x, name=name)
In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # 28x28x1
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = leaky_relu(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        
        x4 = tf.layers.conv2d(relu2, 512, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn4 = tf.layers.batch_normalization(x4, training=True)
        relu4 = leaky_relu(bn4)


        flat = tf.contrib.layers.flatten(relu4)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    
    
    
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 4*4*1024)
        
        x2 = tf.reshape(x1, (-1, 4, 4, 1024))
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)

        x3 = tf.layers.conv2d_transpose(x2, 512, 4, strides=1, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        
        x4 = tf.layers.conv2d_transpose(x3, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = leaky_relu(x4)
        
        x5 = tf.layers.conv2d_transpose(x4, 128, 5, strides=1, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x5 = tf.layers.batch_normalization(x5, training=is_train)
        x5 = leaky_relu(x5)
        

        
        logits = tf.layers.conv2d_transpose(x5, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
    
        out = tf.tanh(logits)
    return out



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, True)
    
    smooth = 0.1
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1.0-smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list = d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list = g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, learn_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    
    
    samples = []
    losses = []
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images*2.0
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )
#                 _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )

                if steps % 20 ==0:
                    train_loss_d = d_loss.eval({input_real:batch_images, input_z:batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    losses.append((train_loss_d, train_loss_g))
                    
                if steps % 100 == 0:
                    
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
    

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5 #this was 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 2.0464... Generator Loss: 2.1851
Epoch 1/2... Discriminator Loss: 1.2579... Generator Loss: 0.6776
Epoch 1/2... Discriminator Loss: 0.9893... Generator Loss: 3.5980
Epoch 1/2... Discriminator Loss: 1.1021... Generator Loss: 4.2374
Epoch 1/2... Discriminator Loss: 1.0388... Generator Loss: 2.1185
Epoch 1/2... Discriminator Loss: 1.6837... Generator Loss: 0.4195
Epoch 1/2... Discriminator Loss: 2.7711... Generator Loss: 0.1570
Epoch 1/2... Discriminator Loss: 2.0325... Generator Loss: 0.6092
Epoch 1/2... Discriminator Loss: 2.7339... Generator Loss: 1.0552
Epoch 1/2... Discriminator Loss: 1.9873... Generator Loss: 0.3678
Epoch 1/2... Discriminator Loss: 1.7625... Generator Loss: 0.6914
Epoch 1/2... Discriminator Loss: 1.8248... Generator Loss: 0.6324
Epoch 1/2... Discriminator Loss: 1.6171... Generator Loss: 0.6138
Epoch 1/2... Discriminator Loss: 1.4148... Generator Loss: 0.5722
Epoch 1/2... Discriminator Loss: 1.4927... Generator Loss: 0.5605
Epoch 1/2... Discriminator Loss: 1.4164... Generator Loss: 0.5724
Epoch 1/2... Discriminator Loss: 1.5325... Generator Loss: 0.7783
Epoch 1/2... Discriminator Loss: 1.7327... Generator Loss: 0.4860
Epoch 1/2... Discriminator Loss: 1.5198... Generator Loss: 1.1312
Epoch 1/2... Discriminator Loss: 1.7076... Generator Loss: 0.4768
Epoch 1/2... Discriminator Loss: 1.6621... Generator Loss: 0.5961
Epoch 1/2... Discriminator Loss: 1.5394... Generator Loss: 0.6284
Epoch 1/2... Discriminator Loss: 1.7127... Generator Loss: 0.6665
Epoch 1/2... Discriminator Loss: 1.5470... Generator Loss: 0.5634
Epoch 1/2... Discriminator Loss: 1.7139... Generator Loss: 0.9615
Epoch 1/2... Discriminator Loss: 1.5398... Generator Loss: 0.6137
Epoch 1/2... Discriminator Loss: 1.5103... Generator Loss: 0.8536
Epoch 1/2... Discriminator Loss: 1.6334... Generator Loss: 0.7560
Epoch 1/2... Discriminator Loss: 1.5755... Generator Loss: 0.5959
Epoch 1/2... Discriminator Loss: 1.5966... Generator Loss: 0.5962
Epoch 1/2... Discriminator Loss: 1.5207... Generator Loss: 0.6464
Epoch 1/2... Discriminator Loss: 1.5792... Generator Loss: 0.6617
Epoch 1/2... Discriminator Loss: 1.6560... Generator Loss: 0.4828
Epoch 1/2... Discriminator Loss: 1.5252... Generator Loss: 0.7959
Epoch 1/2... Discriminator Loss: 1.6421... Generator Loss: 0.6794
Epoch 1/2... Discriminator Loss: 1.7112... Generator Loss: 0.3990
Epoch 1/2... Discriminator Loss: 1.6221... Generator Loss: 0.5595
Epoch 1/2... Discriminator Loss: 1.6539... Generator Loss: 0.4316
Epoch 1/2... Discriminator Loss: 1.6655... Generator Loss: 0.4145
Epoch 1/2... Discriminator Loss: 1.5459... Generator Loss: 0.6533
Epoch 1/2... Discriminator Loss: 1.4799... Generator Loss: 0.5326
Epoch 1/2... Discriminator Loss: 1.5617... Generator Loss: 0.4880
Epoch 1/2... Discriminator Loss: 1.5595... Generator Loss: 0.4861
Epoch 1/2... Discriminator Loss: 1.6156... Generator Loss: 0.4852
Epoch 1/2... Discriminator Loss: 1.6110... Generator Loss: 0.4749
Epoch 1/2... Discriminator Loss: 1.6049... Generator Loss: 0.6250
Epoch 1/2... Discriminator Loss: 1.5406... Generator Loss: 0.7148
Epoch 1/2... Discriminator Loss: 1.5202... Generator Loss: 0.6398
Epoch 1/2... Discriminator Loss: 1.5456... Generator Loss: 0.6577
Epoch 1/2... Discriminator Loss: 1.5776... Generator Loss: 0.4658
Epoch 1/2... Discriminator Loss: 1.5555... Generator Loss: 0.4790
Epoch 1/2... Discriminator Loss: 1.5107... Generator Loss: 0.6190
Epoch 1/2... Discriminator Loss: 1.6200... Generator Loss: 0.4312
Epoch 1/2... Discriminator Loss: 1.5371... Generator Loss: 0.7182
Epoch 1/2... Discriminator Loss: 1.6377... Generator Loss: 0.4080
Epoch 1/2... Discriminator Loss: 1.5024... Generator Loss: 0.6427
Epoch 1/2... Discriminator Loss: 1.5150... Generator Loss: 0.6470
Epoch 1/2... Discriminator Loss: 1.7250... Generator Loss: 0.3525
Epoch 1/2... Discriminator Loss: 1.5594... Generator Loss: 0.8898
Epoch 1/2... Discriminator Loss: 1.5165... Generator Loss: 0.5120
Epoch 1/2... Discriminator Loss: 1.5517... Generator Loss: 0.7939
Epoch 1/2... Discriminator Loss: 1.4631... Generator Loss: 1.1363
Epoch 1/2... Discriminator Loss: 1.4817... Generator Loss: 0.6238
Epoch 1/2... Discriminator Loss: 1.4542... Generator Loss: 0.6382
Epoch 1/2... Discriminator Loss: 1.4348... Generator Loss: 0.7411
Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.7749
Epoch 1/2... Discriminator Loss: 1.4583... Generator Loss: 0.6572
Epoch 1/2... Discriminator Loss: 1.6115... Generator Loss: 0.3971
Epoch 1/2... Discriminator Loss: 1.4251... Generator Loss: 0.7642
Epoch 1/2... Discriminator Loss: 1.4888... Generator Loss: 0.5895
Epoch 1/2... Discriminator Loss: 1.4163... Generator Loss: 0.6280
Epoch 1/2... Discriminator Loss: 1.4090... Generator Loss: 0.6282
Epoch 1/2... Discriminator Loss: 1.3984... Generator Loss: 0.7771
Epoch 1/2... Discriminator Loss: 1.5914... Generator Loss: 0.4942
Epoch 1/2... Discriminator Loss: 1.5083... Generator Loss: 0.5602
Epoch 1/2... Discriminator Loss: 1.4393... Generator Loss: 0.5127
Epoch 1/2... Discriminator Loss: 1.3957... Generator Loss: 0.6804
Epoch 1/2... Discriminator Loss: 1.3704... Generator Loss: 0.9267
Epoch 1/2... Discriminator Loss: 1.5542... Generator Loss: 0.7118
Epoch 1/2... Discriminator Loss: 1.4734... Generator Loss: 0.9828
Epoch 1/2... Discriminator Loss: 1.3828... Generator Loss: 0.6421
Epoch 1/2... Discriminator Loss: 1.5534... Generator Loss: 0.4689
Epoch 1/2... Discriminator Loss: 1.4779... Generator Loss: 0.7284
Epoch 1/2... Discriminator Loss: 1.3822... Generator Loss: 0.6836
Epoch 1/2... Discriminator Loss: 1.3845... Generator Loss: 0.7457
Epoch 1/2... Discriminator Loss: 1.4320... Generator Loss: 0.5059
Epoch 1/2... Discriminator Loss: 1.5601... Generator Loss: 0.4278
Epoch 1/2... Discriminator Loss: 1.6398... Generator Loss: 0.3741
Epoch 1/2... Discriminator Loss: 1.3780... Generator Loss: 0.5964
Epoch 1/2... Discriminator Loss: 1.4619... Generator Loss: 0.4592
Epoch 1/2... Discriminator Loss: 2.0175... Generator Loss: 0.2195
Epoch 1/2... Discriminator Loss: 1.6073... Generator Loss: 0.8464
Epoch 1/2... Discriminator Loss: 1.4857... Generator Loss: 0.5039
Epoch 2/2... Discriminator Loss: 1.3849... Generator Loss: 0.7211
Epoch 2/2... Discriminator Loss: 1.9992... Generator Loss: 0.2474
Epoch 2/2... Discriminator Loss: 1.4064... Generator Loss: 0.8614
Epoch 2/2... Discriminator Loss: 1.3051... Generator Loss: 0.7345
Epoch 2/2... Discriminator Loss: 1.3347... Generator Loss: 0.8051
Epoch 2/2... Discriminator Loss: 1.3877... Generator Loss: 0.5553
Epoch 2/2... Discriminator Loss: 1.5133... Generator Loss: 0.5451
Epoch 2/2... Discriminator Loss: 1.2976... Generator Loss: 0.8866
Epoch 2/2... Discriminator Loss: 1.4627... Generator Loss: 0.9259
Epoch 2/2... Discriminator Loss: 1.3459... Generator Loss: 0.5626
Epoch 2/2... Discriminator Loss: 1.5179... Generator Loss: 0.6523
Epoch 2/2... Discriminator Loss: 1.6015... Generator Loss: 0.3949
Epoch 2/2... Discriminator Loss: 1.4280... Generator Loss: 0.7022
Epoch 2/2... Discriminator Loss: 1.4472... Generator Loss: 0.5539
Epoch 2/2... Discriminator Loss: 1.6409... Generator Loss: 0.5321
Epoch 2/2... Discriminator Loss: 1.7598... Generator Loss: 0.3267
Epoch 2/2... Discriminator Loss: 1.5792... Generator Loss: 0.4219
Epoch 2/2... Discriminator Loss: 1.3456... Generator Loss: 0.6487
Epoch 2/2... Discriminator Loss: 1.4464... Generator Loss: 0.5081
Epoch 2/2... Discriminator Loss: 1.5923... Generator Loss: 0.8373
Epoch 2/2... Discriminator Loss: 1.3890... Generator Loss: 0.6534
Epoch 2/2... Discriminator Loss: 1.5326... Generator Loss: 0.5538
Epoch 2/2... Discriminator Loss: 1.6483... Generator Loss: 0.4300
Epoch 2/2... Discriminator Loss: 1.5237... Generator Loss: 0.4531
Epoch 2/2... Discriminator Loss: 1.5562... Generator Loss: 0.6630
Epoch 2/2... Discriminator Loss: 1.4392... Generator Loss: 0.5133
Epoch 2/2... Discriminator Loss: 1.3019... Generator Loss: 0.7247
Epoch 2/2... Discriminator Loss: 1.3225... Generator Loss: 0.6891
Epoch 2/2... Discriminator Loss: 1.5475... Generator Loss: 0.5111
Epoch 2/2... Discriminator Loss: 1.5732... Generator Loss: 0.4312
Epoch 2/2... Discriminator Loss: 1.5631... Generator Loss: 1.0955
Epoch 2/2... Discriminator Loss: 1.6695... Generator Loss: 0.3891
Epoch 2/2... Discriminator Loss: 1.3732... Generator Loss: 0.5421
Epoch 2/2... Discriminator Loss: 1.4111... Generator Loss: 0.5386
Epoch 2/2... Discriminator Loss: 1.5729... Generator Loss: 0.4150
Epoch 2/2... Discriminator Loss: 1.3746... Generator Loss: 0.5734
Epoch 2/2... Discriminator Loss: 1.6633... Generator Loss: 0.3745
Epoch 2/2... Discriminator Loss: 1.4013... Generator Loss: 0.7293
Epoch 2/2... Discriminator Loss: 1.4247... Generator Loss: 0.7402
Epoch 2/2... Discriminator Loss: 1.2569... Generator Loss: 0.5887
Epoch 2/2... Discriminator Loss: 1.3976... Generator Loss: 0.5195
Epoch 2/2... Discriminator Loss: 1.3136... Generator Loss: 0.7082
Epoch 2/2... Discriminator Loss: 1.4258... Generator Loss: 0.5019
Epoch 2/2... Discriminator Loss: 1.4195... Generator Loss: 0.5155
Epoch 2/2... Discriminator Loss: 1.2765... Generator Loss: 0.7923
Epoch 2/2... Discriminator Loss: 1.3978... Generator Loss: 0.5769
Epoch 2/2... Discriminator Loss: 1.4013... Generator Loss: 0.6600
Epoch 2/2... Discriminator Loss: 1.2053... Generator Loss: 0.8770
Epoch 2/2... Discriminator Loss: 1.2734... Generator Loss: 0.6901
Epoch 2/2... Discriminator Loss: 1.4223... Generator Loss: 0.5520
Epoch 2/2... Discriminator Loss: 1.4288... Generator Loss: 0.5999
Epoch 2/2... Discriminator Loss: 1.4379... Generator Loss: 0.9123
Epoch 2/2... Discriminator Loss: 1.4898... Generator Loss: 0.8571
Epoch 2/2... Discriminator Loss: 1.5615... Generator Loss: 0.4908
Epoch 2/2... Discriminator Loss: 1.4550... Generator Loss: 0.4805
Epoch 2/2... Discriminator Loss: 1.7083... Generator Loss: 0.3823
Epoch 2/2... Discriminator Loss: 1.2422... Generator Loss: 0.8887
Epoch 2/2... Discriminator Loss: 1.5049... Generator Loss: 0.8785
Epoch 2/2... Discriminator Loss: 1.3435... Generator Loss: 0.9730
Epoch 2/2... Discriminator Loss: 1.1746... Generator Loss: 0.9662
Epoch 2/2... Discriminator Loss: 1.5606... Generator Loss: 0.4491
Epoch 2/2... Discriminator Loss: 1.3438... Generator Loss: 1.1137
Epoch 2/2... Discriminator Loss: 1.5688... Generator Loss: 0.7915
Epoch 2/2... Discriminator Loss: 1.3112... Generator Loss: 0.8013
Epoch 2/2... Discriminator Loss: 1.3807... Generator Loss: 0.6110
Epoch 2/2... Discriminator Loss: 1.4073... Generator Loss: 0.6427
Epoch 2/2... Discriminator Loss: 1.4326... Generator Loss: 0.9788
Epoch 2/2... Discriminator Loss: 1.3105... Generator Loss: 0.5926
Epoch 2/2... Discriminator Loss: 1.4397... Generator Loss: 0.5581
Epoch 2/2... Discriminator Loss: 1.2602... Generator Loss: 0.7276
Epoch 2/2... Discriminator Loss: 1.5791... Generator Loss: 0.7686
Epoch 2/2... Discriminator Loss: 1.5065... Generator Loss: 0.6461
Epoch 2/2... Discriminator Loss: 1.4630... Generator Loss: 0.5244
Epoch 2/2... Discriminator Loss: 1.3492... Generator Loss: 0.8175
Epoch 2/2... Discriminator Loss: 1.7220... Generator Loss: 0.3946
Epoch 2/2... Discriminator Loss: 1.4434... Generator Loss: 0.5863
Epoch 2/2... Discriminator Loss: 1.4242... Generator Loss: 0.5909
Epoch 2/2... Discriminator Loss: 1.3466... Generator Loss: 0.7215
Epoch 2/2... Discriminator Loss: 1.2934... Generator Loss: 0.7373
Epoch 2/2... Discriminator Loss: 1.1632... Generator Loss: 0.8688
Epoch 2/2... Discriminator Loss: 1.2363... Generator Loss: 0.6606
Epoch 2/2... Discriminator Loss: 1.6961... Generator Loss: 0.3543
Epoch 2/2... Discriminator Loss: 1.5702... Generator Loss: 0.4576
Epoch 2/2... Discriminator Loss: 1.2857... Generator Loss: 0.9332
Epoch 2/2... Discriminator Loss: 1.5523... Generator Loss: 0.7118
Epoch 2/2... Discriminator Loss: 1.4250... Generator Loss: 0.5295
Epoch 2/2... Discriminator Loss: 1.2263... Generator Loss: 0.8577
Epoch 2/2... Discriminator Loss: 1.4431... Generator Loss: 0.5006
Epoch 2/2... Discriminator Loss: 1.5408... Generator Loss: 0.5893
Epoch 2/2... Discriminator Loss: 1.5766... Generator Loss: 0.5368
Epoch 2/2... Discriminator Loss: 1.5725... Generator Loss: 0.5194
Epoch 2/2... Discriminator Loss: 1.4194... Generator Loss: 0.5982
Epoch 2/2... Discriminator Loss: 1.7915... Generator Loss: 0.3026
Epoch 2/2... Discriminator Loss: 1.6786... Generator Loss: 0.3436

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.3 #this was 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 4.5802... Generator Loss: 0.0569
Epoch 1/2... Discriminator Loss: 3.1071... Generator Loss: 0.1247
Epoch 1/2... Discriminator Loss: 1.7049... Generator Loss: 0.6583
Epoch 1/2... Discriminator Loss: 1.0297... Generator Loss: 1.2424
Epoch 1/2... Discriminator Loss: 0.9804... Generator Loss: 1.4018
Epoch 1/2... Discriminator Loss: 0.8217... Generator Loss: 1.6547
Epoch 1/2... Discriminator Loss: 0.6403... Generator Loss: 2.3800
Epoch 1/2... Discriminator Loss: 2.1992... Generator Loss: 0.3908
Epoch 1/2... Discriminator Loss: 1.8917... Generator Loss: 0.6945
Epoch 1/2... Discriminator Loss: 1.7945... Generator Loss: 0.7847
Epoch 1/2... Discriminator Loss: 1.5573... Generator Loss: 0.5577
Epoch 1/2... Discriminator Loss: 1.9588... Generator Loss: 0.4493
Epoch 1/2... Discriminator Loss: 1.5792... Generator Loss: 0.6512
Epoch 1/2... Discriminator Loss: 1.6078... Generator Loss: 0.6039
Epoch 1/2... Discriminator Loss: 1.6420... Generator Loss: 0.6533
Epoch 1/2... Discriminator Loss: 1.5514... Generator Loss: 0.8001
Epoch 1/2... Discriminator Loss: 1.5603... Generator Loss: 0.6436
Epoch 1/2... Discriminator Loss: 1.6443... Generator Loss: 0.5563
Epoch 1/2... Discriminator Loss: 1.6113... Generator Loss: 0.5882
Epoch 1/2... Discriminator Loss: 1.5142... Generator Loss: 0.6631
Epoch 1/2... Discriminator Loss: 1.6135... Generator Loss: 0.6294
Epoch 1/2... Discriminator Loss: 1.5171... Generator Loss: 0.6373
Epoch 1/2... Discriminator Loss: 1.5611... Generator Loss: 0.7786
Epoch 1/2... Discriminator Loss: 1.5581... Generator Loss: 0.7066
Epoch 1/2... Discriminator Loss: 1.5559... Generator Loss: 0.5869
Epoch 1/2... Discriminator Loss: 1.6456... Generator Loss: 0.6072
Epoch 1/2... Discriminator Loss: 1.4692... Generator Loss: 0.6200
Epoch 1/2... Discriminator Loss: 1.5016... Generator Loss: 0.7418
Epoch 1/2... Discriminator Loss: 1.5183... Generator Loss: 0.6925
Epoch 1/2... Discriminator Loss: 1.5051... Generator Loss: 0.6647
Epoch 1/2... Discriminator Loss: 1.5501... Generator Loss: 0.7119
Epoch 1/2... Discriminator Loss: 1.4846... Generator Loss: 0.6294
Epoch 1/2... Discriminator Loss: 1.5447... Generator Loss: 0.7091
Epoch 1/2... Discriminator Loss: 1.3836... Generator Loss: 0.7566
Epoch 1/2... Discriminator Loss: 1.5593... Generator Loss: 0.6665
Epoch 1/2... Discriminator Loss: 1.4402... Generator Loss: 0.7907
Epoch 1/2... Discriminator Loss: 1.5174... Generator Loss: 0.7252
Epoch 1/2... Discriminator Loss: 1.4692... Generator Loss: 0.7684
Epoch 1/2... Discriminator Loss: 1.5104... Generator Loss: 0.6570
Epoch 1/2... Discriminator Loss: 1.4642... Generator Loss: 0.7116
Epoch 1/2... Discriminator Loss: 1.4767... Generator Loss: 0.7651
Epoch 1/2... Discriminator Loss: 1.4447... Generator Loss: 0.6369
Epoch 1/2... Discriminator Loss: 1.4517... Generator Loss: 0.7716
Epoch 1/2... Discriminator Loss: 1.4109... Generator Loss: 0.7150
Epoch 1/2... Discriminator Loss: 1.4343... Generator Loss: 0.7763
Epoch 1/2... Discriminator Loss: 1.4884... Generator Loss: 0.7180
Epoch 1/2... Discriminator Loss: 1.4831... Generator Loss: 0.7090
Epoch 1/2... Discriminator Loss: 1.4448... Generator Loss: 0.6956
Epoch 1/2... Discriminator Loss: 1.4036... Generator Loss: 0.7996
Epoch 1/2... Discriminator Loss: 1.4786... Generator Loss: 0.6227
Epoch 1/2... Discriminator Loss: 1.4848... Generator Loss: 0.7029
Epoch 1/2... Discriminator Loss: 1.5008... Generator Loss: 0.7123
Epoch 1/2... Discriminator Loss: 1.4128... Generator Loss: 0.7016
Epoch 1/2... Discriminator Loss: 1.4891... Generator Loss: 0.6850
Epoch 1/2... Discriminator Loss: 1.4152... Generator Loss: 0.6708
Epoch 1/2... Discriminator Loss: 1.4991... Generator Loss: 0.7646
Epoch 1/2... Discriminator Loss: 1.4424... Generator Loss: 0.7069
Epoch 1/2... Discriminator Loss: 1.4811... Generator Loss: 0.8128
Epoch 1/2... Discriminator Loss: 1.5020... Generator Loss: 0.7756
Epoch 1/2... Discriminator Loss: 1.4262... Generator Loss: 0.6359
Epoch 1/2... Discriminator Loss: 1.4693... Generator Loss: 0.8015
Epoch 1/2... Discriminator Loss: 1.4867... Generator Loss: 0.9087
Epoch 1/2... Discriminator Loss: 1.4474... Generator Loss: 0.8390
Epoch 1/2... Discriminator Loss: 1.4741... Generator Loss: 0.7045
Epoch 1/2... Discriminator Loss: 1.4545... Generator Loss: 0.6483
Epoch 1/2... Discriminator Loss: 1.5087... Generator Loss: 0.7626
Epoch 1/2... Discriminator Loss: 1.4922... Generator Loss: 0.6308
Epoch 1/2... Discriminator Loss: 1.4486... Generator Loss: 0.7544
Epoch 1/2... Discriminator Loss: 1.4917... Generator Loss: 0.8514
Epoch 1/2... Discriminator Loss: 1.4638... Generator Loss: 0.7018
Epoch 1/2... Discriminator Loss: 1.4362... Generator Loss: 0.7165
Epoch 1/2... Discriminator Loss: 1.4127... Generator Loss: 0.6565
Epoch 1/2... Discriminator Loss: 1.4206... Generator Loss: 0.7705
Epoch 1/2... Discriminator Loss: 1.4836... Generator Loss: 0.6310
Epoch 1/2... Discriminator Loss: 1.4473... Generator Loss: 0.6722
Epoch 1/2... Discriminator Loss: 1.4035... Generator Loss: 0.7969
Epoch 1/2... Discriminator Loss: 1.4310... Generator Loss: 0.6773
Epoch 1/2... Discriminator Loss: 1.4449... Generator Loss: 0.7581
Epoch 1/2... Discriminator Loss: 1.4537... Generator Loss: 0.6992
Epoch 1/2... Discriminator Loss: 1.4157... Generator Loss: 0.7604
Epoch 1/2... Discriminator Loss: 1.4841... Generator Loss: 0.6967
Epoch 1/2... Discriminator Loss: 1.4509... Generator Loss: 0.7414
Epoch 1/2... Discriminator Loss: 1.4482... Generator Loss: 0.8373
Epoch 1/2... Discriminator Loss: 1.4332... Generator Loss: 0.7641
Epoch 1/2... Discriminator Loss: 1.5437... Generator Loss: 0.6224
Epoch 1/2... Discriminator Loss: 1.4542... Generator Loss: 0.6396
Epoch 1/2... Discriminator Loss: 1.4305... Generator Loss: 0.7347
Epoch 1/2... Discriminator Loss: 1.4439... Generator Loss: 0.7024
Epoch 1/2... Discriminator Loss: 1.4189... Generator Loss: 0.7792
Epoch 1/2... Discriminator Loss: 1.4722... Generator Loss: 0.8315
Epoch 1/2... Discriminator Loss: 1.4480... Generator Loss: 0.6782
Epoch 1/2... Discriminator Loss: 1.4470... Generator Loss: 0.7122
Epoch 1/2... Discriminator Loss: 1.4532... Generator Loss: 0.7973
Epoch 1/2... Discriminator Loss: 1.4539... Generator Loss: 0.7472
Epoch 1/2... Discriminator Loss: 1.4334... Generator Loss: 0.8120
Epoch 1/2... Discriminator Loss: 1.4230... Generator Loss: 0.7873
Epoch 1/2... Discriminator Loss: 1.4386... Generator Loss: 0.7522
Epoch 1/2... Discriminator Loss: 1.4258... Generator Loss: 0.7000
Epoch 1/2... Discriminator Loss: 1.4714... Generator Loss: 0.7384
Epoch 1/2... Discriminator Loss: 1.4516... Generator Loss: 0.6913
Epoch 1/2... Discriminator Loss: 1.4633... Generator Loss: 0.7356
Epoch 1/2... Discriminator Loss: 1.4584... Generator Loss: 0.6959
Epoch 1/2... Discriminator Loss: 1.4318... Generator Loss: 0.7880
Epoch 1/2... Discriminator Loss: 1.4413... Generator Loss: 0.7422
Epoch 1/2... Discriminator Loss: 1.4146... Generator Loss: 0.8270
Epoch 1/2... Discriminator Loss: 1.4082... Generator Loss: 0.7010
Epoch 1/2... Discriminator Loss: 1.4206... Generator Loss: 0.7861
Epoch 1/2... Discriminator Loss: 1.4344... Generator Loss: 0.8895
Epoch 1/2... Discriminator Loss: 1.4487... Generator Loss: 0.6693
Epoch 1/2... Discriminator Loss: 1.3476... Generator Loss: 0.7644
Epoch 1/2... Discriminator Loss: 1.4586... Generator Loss: 0.8194
Epoch 1/2... Discriminator Loss: 1.4518... Generator Loss: 0.6863
Epoch 1/2... Discriminator Loss: 1.3942... Generator Loss: 0.7365
Epoch 1/2... Discriminator Loss: 1.4607... Generator Loss: 0.7337
Epoch 1/2... Discriminator Loss: 1.4607... Generator Loss: 0.6942
Epoch 1/2... Discriminator Loss: 1.4710... Generator Loss: 0.8086
Epoch 1/2... Discriminator Loss: 1.4410... Generator Loss: 0.8032
Epoch 1/2... Discriminator Loss: 1.4158... Generator Loss: 0.7324
Epoch 1/2... Discriminator Loss: 1.4616... Generator Loss: 0.8506
Epoch 1/2... Discriminator Loss: 1.4565... Generator Loss: 0.6831
Epoch 1/2... Discriminator Loss: 1.4051... Generator Loss: 0.7889
Epoch 1/2... Discriminator Loss: 1.3956... Generator Loss: 0.7791
Epoch 1/2... Discriminator Loss: 1.4480... Generator Loss: 0.7940
Epoch 1/2... Discriminator Loss: 1.4614... Generator Loss: 0.7508
Epoch 1/2... Discriminator Loss: 1.4093... Generator Loss: 0.7098
Epoch 1/2... Discriminator Loss: 1.4160... Generator Loss: 0.7229
Epoch 1/2... Discriminator Loss: 1.4500... Generator Loss: 0.7279
Epoch 1/2... Discriminator Loss: 1.4359... Generator Loss: 0.7844
Epoch 1/2... Discriminator Loss: 1.4648... Generator Loss: 0.6224
Epoch 1/2... Discriminator Loss: 1.4257... Generator Loss: 0.7813
Epoch 1/2... Discriminator Loss: 1.3877... Generator Loss: 0.7339
Epoch 1/2... Discriminator Loss: 1.4023... Generator Loss: 0.7585
Epoch 1/2... Discriminator Loss: 1.4421... Generator Loss: 0.8924
Epoch 1/2... Discriminator Loss: 1.4307... Generator Loss: 0.6792
Epoch 1/2... Discriminator Loss: 1.4344... Generator Loss: 0.6939
Epoch 1/2... Discriminator Loss: 1.3894... Generator Loss: 0.7149
Epoch 1/2... Discriminator Loss: 1.4020... Generator Loss: 0.7859
Epoch 1/2... Discriminator Loss: 1.4039... Generator Loss: 0.7427
Epoch 1/2... Discriminator Loss: 1.4364... Generator Loss: 0.7002
Epoch 1/2... Discriminator Loss: 1.4297... Generator Loss: 0.7977
Epoch 1/2... Discriminator Loss: 1.4600... Generator Loss: 0.7402
Epoch 1/2... Discriminator Loss: 1.4822... Generator Loss: 0.8250
Epoch 1/2... Discriminator Loss: 1.4098... Generator Loss: 0.8163
Epoch 1/2... Discriminator Loss: 1.4151... Generator Loss: 0.6623
Epoch 1/2... Discriminator Loss: 1.3917... Generator Loss: 0.6919
Epoch 1/2... Discriminator Loss: 1.4649... Generator Loss: 0.6597
Epoch 1/2... Discriminator Loss: 1.4701... Generator Loss: 0.7999
Epoch 1/2... Discriminator Loss: 1.4436... Generator Loss: 0.7394
Epoch 1/2... Discriminator Loss: 1.4535... Generator Loss: 0.6642
Epoch 1/2... Discriminator Loss: 1.3823... Generator Loss: 0.7807
Epoch 1/2... Discriminator Loss: 1.4494... Generator Loss: 0.7924
Epoch 1/2... Discriminator Loss: 1.4139... Generator Loss: 0.7358
Epoch 1/2... Discriminator Loss: 1.4442... Generator Loss: 0.7110
Epoch 1/2... Discriminator Loss: 1.4493... Generator Loss: 0.6665
Epoch 1/2... Discriminator Loss: 1.3860... Generator Loss: 0.7880
Epoch 1/2... Discriminator Loss: 1.4064... Generator Loss: 0.7629
Epoch 1/2... Discriminator Loss: 1.4502... Generator Loss: 0.6214
Epoch 1/2... Discriminator Loss: 1.3959... Generator Loss: 0.7756
Epoch 1/2... Discriminator Loss: 1.3697... Generator Loss: 0.7620
Epoch 1/2... Discriminator Loss: 1.4220... Generator Loss: 0.7538
Epoch 1/2... Discriminator Loss: 1.4302... Generator Loss: 0.6632
Epoch 1/2... Discriminator Loss: 1.4061... Generator Loss: 0.7974
Epoch 1/2... Discriminator Loss: 1.3898... Generator Loss: 0.8202
Epoch 1/2... Discriminator Loss: 1.4328... Generator Loss: 0.7488
Epoch 1/2... Discriminator Loss: 1.4350... Generator Loss: 0.7134
Epoch 1/2... Discriminator Loss: 1.4147... Generator Loss: 0.7037
Epoch 1/2... Discriminator Loss: 1.4042... Generator Loss: 0.7545
Epoch 1/2... Discriminator Loss: 1.4438... Generator Loss: 0.7015
Epoch 1/2... Discriminator Loss: 1.4453... Generator Loss: 0.6679
Epoch 1/2... Discriminator Loss: 1.4543... Generator Loss: 0.6781
Epoch 1/2... Discriminator Loss: 1.4197... Generator Loss: 0.6797
Epoch 1/2... Discriminator Loss: 1.4205... Generator Loss: 0.7380
Epoch 1/2... Discriminator Loss: 1.4100... Generator Loss: 0.8106
Epoch 1/2... Discriminator Loss: 1.4433... Generator Loss: 0.7441
Epoch 1/2... Discriminator Loss: 1.4268... Generator Loss: 0.7274
Epoch 1/2... Discriminator Loss: 1.4418... Generator Loss: 0.7084
Epoch 1/2... Discriminator Loss: 1.4258... Generator Loss: 0.6834
Epoch 1/2... Discriminator Loss: 1.4371... Generator Loss: 0.8080
Epoch 1/2... Discriminator Loss: 1.3913... Generator Loss: 0.8923
Epoch 1/2... Discriminator Loss: 1.4062... Generator Loss: 0.6999
Epoch 1/2... Discriminator Loss: 1.4219... Generator Loss: 0.7243
Epoch 1/2... Discriminator Loss: 1.4077... Generator Loss: 0.8088
Epoch 1/2... Discriminator Loss: 1.4037... Generator Loss: 0.7217
Epoch 1/2... Discriminator Loss: 1.3889... Generator Loss: 0.8421
Epoch 1/2... Discriminator Loss: 1.4267... Generator Loss: 0.8702
Epoch 1/2... Discriminator Loss: 1.4354... Generator Loss: 0.6359
Epoch 1/2... Discriminator Loss: 1.4257... Generator Loss: 0.6702
Epoch 1/2... Discriminator Loss: 1.4410... Generator Loss: 0.7627
Epoch 1/2... Discriminator Loss: 1.4504... Generator Loss: 0.7094
Epoch 1/2... Discriminator Loss: 1.4090... Generator Loss: 0.7594
Epoch 1/2... Discriminator Loss: 1.3973... Generator Loss: 0.7674
Epoch 1/2... Discriminator Loss: 1.4200... Generator Loss: 0.7183
Epoch 1/2... Discriminator Loss: 1.4626... Generator Loss: 0.7818
Epoch 1/2... Discriminator Loss: 1.4405... Generator Loss: 0.6367
Epoch 1/2... Discriminator Loss: 1.4354... Generator Loss: 0.7419
Epoch 1/2... Discriminator Loss: 1.4319... Generator Loss: 0.7981
Epoch 1/2... Discriminator Loss: 1.4491... Generator Loss: 0.7714
Epoch 1/2... Discriminator Loss: 1.4065... Generator Loss: 0.7478
Epoch 1/2... Discriminator Loss: 1.3914... Generator Loss: 0.6863
Epoch 1/2... Discriminator Loss: 1.4114... Generator Loss: 0.7731
Epoch 1/2... Discriminator Loss: 1.4159... Generator Loss: 0.8113
Epoch 1/2... Discriminator Loss: 1.4441... Generator Loss: 0.5921
Epoch 1/2... Discriminator Loss: 1.4282... Generator Loss: 0.6931
Epoch 1/2... Discriminator Loss: 1.3847... Generator Loss: 0.7787
Epoch 1/2... Discriminator Loss: 1.4246... Generator Loss: 0.7279
Epoch 1/2... Discriminator Loss: 1.4045... Generator Loss: 0.7624
Epoch 1/2... Discriminator Loss: 1.4129... Generator Loss: 0.7308
Epoch 1/2... Discriminator Loss: 1.4010... Generator Loss: 0.7360
Epoch 1/2... Discriminator Loss: 1.4350... Generator Loss: 0.8505
Epoch 1/2... Discriminator Loss: 1.4465... Generator Loss: 0.8177
Epoch 1/2... Discriminator Loss: 1.4183... Generator Loss: 0.7259
Epoch 1/2... Discriminator Loss: 1.3862... Generator Loss: 0.7680
Epoch 1/2... Discriminator Loss: 1.4197... Generator Loss: 0.7642
Epoch 1/2... Discriminator Loss: 1.4321... Generator Loss: 0.7738
Epoch 1/2... Discriminator Loss: 1.3786... Generator Loss: 0.7784
Epoch 1/2... Discriminator Loss: 1.4531... Generator Loss: 0.6894
Epoch 1/2... Discriminator Loss: 1.3860... Generator Loss: 0.7720
Epoch 1/2... Discriminator Loss: 1.3830... Generator Loss: 0.7918
Epoch 1/2... Discriminator Loss: 1.3728... Generator Loss: 0.8275
Epoch 1/2... Discriminator Loss: 1.3959... Generator Loss: 0.8002
Epoch 1/2... Discriminator Loss: 1.4339... Generator Loss: 0.6919
Epoch 1/2... Discriminator Loss: 1.3904... Generator Loss: 0.7410
Epoch 1/2... Discriminator Loss: 1.4020... Generator Loss: 0.6553
Epoch 1/2... Discriminator Loss: 1.3953... Generator Loss: 0.7788
Epoch 1/2... Discriminator Loss: 1.4107... Generator Loss: 0.7583
Epoch 1/2... Discriminator Loss: 1.3855... Generator Loss: 0.8322
Epoch 1/2... Discriminator Loss: 1.4368... Generator Loss: 0.6123
Epoch 1/2... Discriminator Loss: 1.4424... Generator Loss: 0.6397
Epoch 1/2... Discriminator Loss: 1.4115... Generator Loss: 0.7442
Epoch 1/2... Discriminator Loss: 1.3907... Generator Loss: 0.7114
Epoch 1/2... Discriminator Loss: 1.3921... Generator Loss: 0.7924
Epoch 1/2... Discriminator Loss: 1.3855... Generator Loss: 0.7346
Epoch 1/2... Discriminator Loss: 1.4256... Generator Loss: 0.8936
Epoch 1/2... Discriminator Loss: 1.4060... Generator Loss: 0.6804
Epoch 1/2... Discriminator Loss: 1.4210... Generator Loss: 0.9042
Epoch 1/2... Discriminator Loss: 1.4002... Generator Loss: 0.7184
Epoch 1/2... Discriminator Loss: 1.4021... Generator Loss: 0.6634
Epoch 1/2... Discriminator Loss: 1.4190... Generator Loss: 0.7596
Epoch 1/2... Discriminator Loss: 1.3795... Generator Loss: 0.7941
Epoch 1/2... Discriminator Loss: 1.3984... Generator Loss: 0.7773
Epoch 1/2... Discriminator Loss: 1.4227... Generator Loss: 0.8566
Epoch 1/2... Discriminator Loss: 1.4119... Generator Loss: 0.7402
Epoch 1/2... Discriminator Loss: 1.4286... Generator Loss: 0.7573
Epoch 1/2... Discriminator Loss: 1.4523... Generator Loss: 0.6415
Epoch 1/2... Discriminator Loss: 1.4072... Generator Loss: 0.8408
Epoch 1/2... Discriminator Loss: 1.4033... Generator Loss: 0.7621
Epoch 1/2... Discriminator Loss: 1.3583... Generator Loss: 0.8148
Epoch 1/2... Discriminator Loss: 1.4402... Generator Loss: 0.8228
Epoch 1/2... Discriminator Loss: 1.3876... Generator Loss: 0.6875
Epoch 1/2... Discriminator Loss: 1.4060... Generator Loss: 0.7733
Epoch 1/2... Discriminator Loss: 1.3833... Generator Loss: 0.7244
Epoch 1/2... Discriminator Loss: 1.4190... Generator Loss: 0.7033
Epoch 1/2... Discriminator Loss: 1.3981... Generator Loss: 0.7618
Epoch 1/2... Discriminator Loss: 1.4074... Generator Loss: 0.8286
Epoch 1/2... Discriminator Loss: 1.4174... Generator Loss: 0.7721
Epoch 1/2... Discriminator Loss: 1.4136... Generator Loss: 0.8637
Epoch 1/2... Discriminator Loss: 1.4154... Generator Loss: 0.7188
Epoch 1/2... Discriminator Loss: 1.4399... Generator Loss: 0.6541
Epoch 1/2... Discriminator Loss: 1.4271... Generator Loss: 0.6783
Epoch 1/2... Discriminator Loss: 1.3964... Generator Loss: 0.8453
Epoch 1/2... Discriminator Loss: 1.3911... Generator Loss: 0.7724
Epoch 1/2... Discriminator Loss: 1.3991... Generator Loss: 0.7191
Epoch 1/2... Discriminator Loss: 1.4251... Generator Loss: 0.7934
Epoch 1/2... Discriminator Loss: 1.4121... Generator Loss: 0.6984
Epoch 1/2... Discriminator Loss: 1.3972... Generator Loss: 0.6943
Epoch 1/2... Discriminator Loss: 1.4258... Generator Loss: 0.7011
Epoch 1/2... Discriminator Loss: 1.4332... Generator Loss: 0.8428
Epoch 1/2... Discriminator Loss: 1.4257... Generator Loss: 0.7669
Epoch 1/2... Discriminator Loss: 1.4204... Generator Loss: 0.8205
Epoch 1/2... Discriminator Loss: 1.4216... Generator Loss: 0.7438
Epoch 1/2... Discriminator Loss: 1.3742... Generator Loss: 0.7383
Epoch 1/2... Discriminator Loss: 1.3926... Generator Loss: 0.7201
Epoch 1/2... Discriminator Loss: 1.4042... Generator Loss: 0.7543
Epoch 1/2... Discriminator Loss: 1.3989... Generator Loss: 0.7028
Epoch 1/2... Discriminator Loss: 1.3887... Generator Loss: 0.7466
Epoch 1/2... Discriminator Loss: 1.4275... Generator Loss: 0.7020
Epoch 1/2... Discriminator Loss: 1.3706... Generator Loss: 0.7592
Epoch 1/2... Discriminator Loss: 1.3916... Generator Loss: 0.7323
Epoch 1/2... Discriminator Loss: 1.4185... Generator Loss: 0.7415
Epoch 1/2... Discriminator Loss: 1.3762... Generator Loss: 0.8495
Epoch 1/2... Discriminator Loss: 1.4141... Generator Loss: 0.7496
Epoch 1/2... Discriminator Loss: 1.4049... Generator Loss: 0.7938
Epoch 1/2... Discriminator Loss: 1.4148... Generator Loss: 0.7272
Epoch 1/2... Discriminator Loss: 1.4089... Generator Loss: 0.7108
Epoch 1/2... Discriminator Loss: 1.4358... Generator Loss: 0.9427
Epoch 1/2... Discriminator Loss: 1.3877... Generator Loss: 0.7436
Epoch 1/2... Discriminator Loss: 1.4011... Generator Loss: 0.7654
Epoch 1/2... Discriminator Loss: 1.4049... Generator Loss: 0.7366
Epoch 1/2... Discriminator Loss: 1.4256... Generator Loss: 0.7249
Epoch 1/2... Discriminator Loss: 1.4133... Generator Loss: 0.7460
Epoch 1/2... Discriminator Loss: 1.3999... Generator Loss: 0.6993
Epoch 1/2... Discriminator Loss: 1.4067... Generator Loss: 0.9465
Epoch 1/2... Discriminator Loss: 1.4013... Generator Loss: 0.6474
Epoch 1/2... Discriminator Loss: 1.4007... Generator Loss: 0.6691
Epoch 1/2... Discriminator Loss: 1.4236... Generator Loss: 0.7753
Epoch 1/2... Discriminator Loss: 1.3849... Generator Loss: 0.8906
Epoch 1/2... Discriminator Loss: 1.3878... Generator Loss: 0.8078
Epoch 1/2... Discriminator Loss: 1.4141... Generator Loss: 0.7804
Epoch 1/2... Discriminator Loss: 1.4002... Generator Loss: 0.8121
Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.7471
Epoch 1/2... Discriminator Loss: 1.4021... Generator Loss: 0.6914
Epoch 1/2... Discriminator Loss: 1.4281... Generator Loss: 0.8065
Epoch 1/2... Discriminator Loss: 1.3867... Generator Loss: 0.7959
Epoch 1/2... Discriminator Loss: 1.4148... Generator Loss: 0.7833
Epoch 1/2... Discriminator Loss: 1.3984... Generator Loss: 0.8649
Epoch 1/2... Discriminator Loss: 1.4008... Generator Loss: 0.6745
Epoch 1/2... Discriminator Loss: 1.4108... Generator Loss: 0.6699
Epoch 1/2... Discriminator Loss: 1.3992... Generator Loss: 0.7194
Epoch 1/2... Discriminator Loss: 1.4350... Generator Loss: 0.6545
Epoch 1/2... Discriminator Loss: 1.3932... Generator Loss: 0.6984
Epoch 1/2... Discriminator Loss: 1.3840... Generator Loss: 0.8177
Epoch 1/2... Discriminator Loss: 1.4174... Generator Loss: 0.7500
Epoch 1/2... Discriminator Loss: 1.4118... Generator Loss: 0.6851
Epoch 1/2... Discriminator Loss: 1.3815... Generator Loss: 0.8265
Epoch 1/2... Discriminator Loss: 1.4136... Generator Loss: 0.6673
Epoch 1/2... Discriminator Loss: 1.3912... Generator Loss: 0.7537
Epoch 2/2... Discriminator Loss: 1.4218... Generator Loss: 0.8523
Epoch 2/2... Discriminator Loss: 1.3937... Generator Loss: 0.7298
Epoch 2/2... Discriminator Loss: 1.3854... Generator Loss: 0.8338
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.7572
Epoch 2/2... Discriminator Loss: 1.4124... Generator Loss: 0.7918
Epoch 2/2... Discriminator Loss: 1.4019... Generator Loss: 0.7870
Epoch 2/2... Discriminator Loss: 1.4439... Generator Loss: 0.7423
Epoch 2/2... Discriminator Loss: 1.3937... Generator Loss: 0.8292
Epoch 2/2... Discriminator Loss: 1.4392... Generator Loss: 0.7076
Epoch 2/2... Discriminator Loss: 1.4062... Generator Loss: 0.6644
Epoch 2/2... Discriminator Loss: 1.3663... Generator Loss: 0.7821
Epoch 2/2... Discriminator Loss: 1.3707... Generator Loss: 0.8110
Epoch 2/2... Discriminator Loss: 1.4082... Generator Loss: 0.7698
Epoch 2/2... Discriminator Loss: 1.4618... Generator Loss: 0.6961
Epoch 2/2... Discriminator Loss: 1.3856... Generator Loss: 0.7615
Epoch 2/2... Discriminator Loss: 1.3869... Generator Loss: 0.9547
Epoch 2/2... Discriminator Loss: 1.3982... Generator Loss: 0.7501
Epoch 2/2... Discriminator Loss: 1.4119... Generator Loss: 0.7437
Epoch 2/2... Discriminator Loss: 1.4114... Generator Loss: 0.7369
Epoch 2/2... Discriminator Loss: 1.3983... Generator Loss: 0.8177
Epoch 2/2... Discriminator Loss: 1.3972... Generator Loss: 0.7746
Epoch 2/2... Discriminator Loss: 1.4014... Generator Loss: 0.6991
Epoch 2/2... Discriminator Loss: 1.4040... Generator Loss: 0.8643
Epoch 2/2... Discriminator Loss: 1.4059... Generator Loss: 0.7380
Epoch 2/2... Discriminator Loss: 1.4047... Generator Loss: 0.6882
Epoch 2/2... Discriminator Loss: 1.4003... Generator Loss: 0.7886
Epoch 2/2... Discriminator Loss: 1.4078... Generator Loss: 0.7070
Epoch 2/2... Discriminator Loss: 1.4068... Generator Loss: 0.7411
Epoch 2/2... Discriminator Loss: 1.3767... Generator Loss: 0.8855
Epoch 2/2... Discriminator Loss: 1.4183... Generator Loss: 0.6953
Epoch 2/2... Discriminator Loss: 1.3852... Generator Loss: 0.7526
Epoch 2/2... Discriminator Loss: 1.4830... Generator Loss: 0.5427
Epoch 2/2... Discriminator Loss: 1.4029... Generator Loss: 0.8966
Epoch 2/2... Discriminator Loss: 1.4174... Generator Loss: 0.7969
Epoch 2/2... Discriminator Loss: 1.4182... Generator Loss: 0.7459
Epoch 2/2... Discriminator Loss: 1.4187... Generator Loss: 0.7828
Epoch 2/2... Discriminator Loss: 1.3990... Generator Loss: 0.9291
Epoch 2/2... Discriminator Loss: 1.3876... Generator Loss: 0.7423
Epoch 2/2... Discriminator Loss: 1.3902... Generator Loss: 0.7361
Epoch 2/2... Discriminator Loss: 1.3818... Generator Loss: 0.7603
Epoch 2/2... Discriminator Loss: 1.4154... Generator Loss: 0.6128
Epoch 2/2... Discriminator Loss: 1.4307... Generator Loss: 0.8605
Epoch 2/2... Discriminator Loss: 1.4071... Generator Loss: 0.6816
Epoch 2/2... Discriminator Loss: 1.4145... Generator Loss: 0.7633
Epoch 2/2... Discriminator Loss: 1.3899... Generator Loss: 0.8902
Epoch 2/2... Discriminator Loss: 1.4280... Generator Loss: 0.6590
Epoch 2/2... Discriminator Loss: 1.4267... Generator Loss: 0.8534
Epoch 2/2... Discriminator Loss: 1.4012... Generator Loss: 0.7984
Epoch 2/2... Discriminator Loss: 1.3902... Generator Loss: 0.8140
Epoch 2/2... Discriminator Loss: 1.4093... Generator Loss: 0.7560
Epoch 2/2... Discriminator Loss: 1.3629... Generator Loss: 0.7576
Epoch 2/2... Discriminator Loss: 1.3913... Generator Loss: 0.8252
Epoch 2/2... Discriminator Loss: 1.4027... Generator Loss: 0.7712
Epoch 2/2... Discriminator Loss: 1.3934... Generator Loss: 0.7888
Epoch 2/2... Discriminator Loss: 1.3975... Generator Loss: 0.7398
Epoch 2/2... Discriminator Loss: 1.3844... Generator Loss: 0.7526
Epoch 2/2... Discriminator Loss: 1.4039... Generator Loss: 0.8034
Epoch 2/2... Discriminator Loss: 1.3925... Generator Loss: 0.7703
Epoch 2/2... Discriminator Loss: 1.3957... Generator Loss: 0.8387
Epoch 2/2... Discriminator Loss: 1.4037... Generator Loss: 0.7989
Epoch 2/2... Discriminator Loss: 1.3929... Generator Loss: 0.8783
Epoch 2/2... Discriminator Loss: 1.3948... Generator Loss: 0.9161
Epoch 2/2... Discriminator Loss: 1.3783... Generator Loss: 0.8206
Epoch 2/2... Discriminator Loss: 1.3943... Generator Loss: 0.7863
Epoch 2/2... Discriminator Loss: 1.3913... Generator Loss: 0.7624
Epoch 2/2... Discriminator Loss: 1.3827... Generator Loss: 0.7536
Epoch 2/2... Discriminator Loss: 1.3798... Generator Loss: 0.8784
Epoch 2/2... Discriminator Loss: 1.3923... Generator Loss: 0.8130
Epoch 2/2... Discriminator Loss: 1.3948... Generator Loss: 0.7554
Epoch 2/2... Discriminator Loss: 1.4137... Generator Loss: 0.8198
Epoch 2/2... Discriminator Loss: 1.3959... Generator Loss: 0.7607
Epoch 2/2... Discriminator Loss: 1.3963... Generator Loss: 0.7411
Epoch 2/2... Discriminator Loss: 1.3914... Generator Loss: 0.7698
Epoch 2/2... Discriminator Loss: 1.3767... Generator Loss: 0.7970
Epoch 2/2... Discriminator Loss: 1.3766... Generator Loss: 0.8551
Epoch 2/2... Discriminator Loss: 1.3829... Generator Loss: 0.7728
Epoch 2/2... Discriminator Loss: 1.3847... Generator Loss: 0.7221
Epoch 2/2... Discriminator Loss: 1.3884... Generator Loss: 0.7277
Epoch 2/2... Discriminator Loss: 1.3932... Generator Loss: 0.8580
Epoch 2/2... Discriminator Loss: 1.4031... Generator Loss: 0.7660
Epoch 2/2... Discriminator Loss: 1.3806... Generator Loss: 0.7442
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8338
Epoch 2/2... Discriminator Loss: 1.3890... Generator Loss: 0.7585
Epoch 2/2... Discriminator Loss: 1.4013... Generator Loss: 0.7596
Epoch 2/2... Discriminator Loss: 1.3954... Generator Loss: 0.7637
Epoch 2/2... Discriminator Loss: 1.3978... Generator Loss: 0.7280
Epoch 2/2... Discriminator Loss: 1.4050... Generator Loss: 0.8103
Epoch 2/2... Discriminator Loss: 1.3982... Generator Loss: 0.7575
Epoch 2/2... Discriminator Loss: 1.4012... Generator Loss: 0.7517
Epoch 2/2... Discriminator Loss: 1.3866... Generator Loss: 0.7995
Epoch 2/2... Discriminator Loss: 1.3902... Generator Loss: 0.8832
Epoch 2/2... Discriminator Loss: 1.3896... Generator Loss: 0.8347
Epoch 2/2... Discriminator Loss: 1.3724... Generator Loss: 0.8402
Epoch 2/2... Discriminator Loss: 1.4209... Generator Loss: 0.6969
Epoch 2/2... Discriminator Loss: 1.4062... Generator Loss: 0.7009
Epoch 2/2... Discriminator Loss: 1.4073... Generator Loss: 0.8173
Epoch 2/2... Discriminator Loss: 1.3819... Generator Loss: 0.7871
Epoch 2/2... Discriminator Loss: 1.3990... Generator Loss: 0.6941
Epoch 2/2... Discriminator Loss: 1.4075... Generator Loss: 0.8060
Epoch 2/2... Discriminator Loss: 1.3873... Generator Loss: 0.7346
Epoch 2/2... Discriminator Loss: 1.4205... Generator Loss: 0.6352
Epoch 2/2... Discriminator Loss: 1.4004... Generator Loss: 0.8773
Epoch 2/2... Discriminator Loss: 1.4036... Generator Loss: 0.7429
Epoch 2/2... Discriminator Loss: 1.4003... Generator Loss: 0.7003
Epoch 2/2... Discriminator Loss: 1.3843... Generator Loss: 0.8655
Epoch 2/2... Discriminator Loss: 1.3803... Generator Loss: 0.8077
Epoch 2/2... Discriminator Loss: 1.4039... Generator Loss: 0.8013
Epoch 2/2... Discriminator Loss: 1.3912... Generator Loss: 0.7801
Epoch 2/2... Discriminator Loss: 1.3933... Generator Loss: 0.7252
Epoch 2/2... Discriminator Loss: 1.3936... Generator Loss: 0.9432
Epoch 2/2... Discriminator Loss: 1.3964... Generator Loss: 0.8078
Epoch 2/2... Discriminator Loss: 1.3942... Generator Loss: 0.7997
Epoch 2/2... Discriminator Loss: 1.3699... Generator Loss: 0.8321
Epoch 2/2... Discriminator Loss: 1.4169... Generator Loss: 0.7289
Epoch 2/2... Discriminator Loss: 1.3918... Generator Loss: 0.8328
Epoch 2/2... Discriminator Loss: 1.3932... Generator Loss: 0.7994
Epoch 2/2... Discriminator Loss: 1.4080... Generator Loss: 0.7993
Epoch 2/2... Discriminator Loss: 1.3967... Generator Loss: 0.8713
Epoch 2/2... Discriminator Loss: 1.3807... Generator Loss: 0.6726
Epoch 2/2... Discriminator Loss: 1.4004... Generator Loss: 0.7854
Epoch 2/2... Discriminator Loss: 1.4064... Generator Loss: 0.8637
Epoch 2/2... Discriminator Loss: 1.3890... Generator Loss: 0.7592
Epoch 2/2... Discriminator Loss: 1.3849... Generator Loss: 0.7987
Epoch 2/2... Discriminator Loss: 1.3918... Generator Loss: 0.6669
Epoch 2/2... Discriminator Loss: 1.4115... Generator Loss: 0.7937
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.7601
Epoch 2/2... Discriminator Loss: 1.4014... Generator Loss: 0.8485
Epoch 2/2... Discriminator Loss: 1.3832... Generator Loss: 0.7542
Epoch 2/2... Discriminator Loss: 1.3834... Generator Loss: 0.7397
Epoch 2/2... Discriminator Loss: 1.4007... Generator Loss: 0.6844
Epoch 2/2... Discriminator Loss: 1.3815... Generator Loss: 0.7550
Epoch 2/2... Discriminator Loss: 1.3928... Generator Loss: 0.6569
Epoch 2/2... Discriminator Loss: 1.3803... Generator Loss: 0.8036
Epoch 2/2... Discriminator Loss: 1.3885... Generator Loss: 0.7693
Epoch 2/2... Discriminator Loss: 1.4074... Generator Loss: 0.6293
Epoch 2/2... Discriminator Loss: 1.3791... Generator Loss: 0.7934
Epoch 2/2... Discriminator Loss: 1.3840... Generator Loss: 0.8194
Epoch 2/2... Discriminator Loss: 1.3991... Generator Loss: 0.7073
Epoch 2/2... Discriminator Loss: 1.3897... Generator Loss: 0.8593
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8180
Epoch 2/2... Discriminator Loss: 1.3918... Generator Loss: 0.7246
Epoch 2/2... Discriminator Loss: 1.3995... Generator Loss: 0.7611
Epoch 2/2... Discriminator Loss: 1.4037... Generator Loss: 0.7238
Epoch 2/2... Discriminator Loss: 1.3951... Generator Loss: 0.6339
Epoch 2/2... Discriminator Loss: 1.4007... Generator Loss: 0.8434
Epoch 2/2... Discriminator Loss: 1.4122... Generator Loss: 0.7595
Epoch 2/2... Discriminator Loss: 1.3905... Generator Loss: 0.6957
Epoch 2/2... Discriminator Loss: 1.4025... Generator Loss: 0.7041
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8215
Epoch 2/2... Discriminator Loss: 1.3884... Generator Loss: 0.7605
Epoch 2/2... Discriminator Loss: 1.3933... Generator Loss: 0.8084
Epoch 2/2... Discriminator Loss: 1.4058... Generator Loss: 0.7856
Epoch 2/2... Discriminator Loss: 1.4128... Generator Loss: 0.8362
Epoch 2/2... Discriminator Loss: 1.3742... Generator Loss: 0.7274
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.7922
Epoch 2/2... Discriminator Loss: 1.3805... Generator Loss: 0.6976
Epoch 2/2... Discriminator Loss: 1.3976... Generator Loss: 0.8276
Epoch 2/2... Discriminator Loss: 1.3832... Generator Loss: 0.7110
Epoch 2/2... Discriminator Loss: 1.4019... Generator Loss: 0.7563
Epoch 2/2... Discriminator Loss: 1.3942... Generator Loss: 0.6413
Epoch 2/2... Discriminator Loss: 1.3763... Generator Loss: 0.7767
Epoch 2/2... Discriminator Loss: 1.3729... Generator Loss: 0.7794
Epoch 2/2... Discriminator Loss: 1.3908... Generator Loss: 0.6997
Epoch 2/2... Discriminator Loss: 1.3986... Generator Loss: 0.8690
Epoch 2/2... Discriminator Loss: 1.3977... Generator Loss: 0.7786
Epoch 2/2... Discriminator Loss: 1.3950... Generator Loss: 0.8260
Epoch 2/2... Discriminator Loss: 1.3915... Generator Loss: 0.7704
Epoch 2/2... Discriminator Loss: 1.3799... Generator Loss: 0.8066
Epoch 2/2... Discriminator Loss: 1.3925... Generator Loss: 0.7670
Epoch 2/2... Discriminator Loss: 1.3833... Generator Loss: 0.8103
Epoch 2/2... Discriminator Loss: 1.3839... Generator Loss: 0.7735
Epoch 2/2... Discriminator Loss: 1.3855... Generator Loss: 0.7027
Epoch 2/2... Discriminator Loss: 1.3799... Generator Loss: 0.7386
Epoch 2/2... Discriminator Loss: 1.4055... Generator Loss: 0.8456
Epoch 2/2... Discriminator Loss: 1.4140... Generator Loss: 0.8074
Epoch 2/2... Discriminator Loss: 1.3827... Generator Loss: 0.8212
Epoch 2/2... Discriminator Loss: 1.3708... Generator Loss: 0.8273
Epoch 2/2... Discriminator Loss: 1.3818... Generator Loss: 0.7272
Epoch 2/2... Discriminator Loss: 1.3852... Generator Loss: 0.7282
Epoch 2/2... Discriminator Loss: 1.4062... Generator Loss: 0.8781
Epoch 2/2... Discriminator Loss: 1.3999... Generator Loss: 0.7580
Epoch 2/2... Discriminator Loss: 1.3861... Generator Loss: 0.7939
Epoch 2/2... Discriminator Loss: 1.3906... Generator Loss: 0.7911
Epoch 2/2... Discriminator Loss: 1.3865... Generator Loss: 0.8493
Epoch 2/2... Discriminator Loss: 1.3825... Generator Loss: 0.7614
Epoch 2/2... Discriminator Loss: 1.4043... Generator Loss: 0.8563
Epoch 2/2... Discriminator Loss: 1.3724... Generator Loss: 0.8451
Epoch 2/2... Discriminator Loss: 1.3883... Generator Loss: 0.7871
Epoch 2/2... Discriminator Loss: 1.3920... Generator Loss: 0.7874
Epoch 2/2... Discriminator Loss: 1.3935... Generator Loss: 0.7461
Epoch 2/2... Discriminator Loss: 1.3942... Generator Loss: 0.8632
Epoch 2/2... Discriminator Loss: 1.3885... Generator Loss: 0.8386
Epoch 2/2... Discriminator Loss: 1.3863... Generator Loss: 0.8260
Epoch 2/2... Discriminator Loss: 1.3956... Generator Loss: 0.7905
Epoch 2/2... Discriminator Loss: 1.3885... Generator Loss: 0.7393
Epoch 2/2... Discriminator Loss: 1.3782... Generator Loss: 0.8074
Epoch 2/2... Discriminator Loss: 1.3790... Generator Loss: 0.7357
Epoch 2/2... Discriminator Loss: 1.3732... Generator Loss: 0.7552
Epoch 2/2... Discriminator Loss: 1.3925... Generator Loss: 0.8305
Epoch 2/2... Discriminator Loss: 1.4060... Generator Loss: 0.7753
Epoch 2/2... Discriminator Loss: 1.3962... Generator Loss: 0.7720
Epoch 2/2... Discriminator Loss: 1.4290... Generator Loss: 0.7615
Epoch 2/2... Discriminator Loss: 1.3675... Generator Loss: 0.7260
Epoch 2/2... Discriminator Loss: 1.3927... Generator Loss: 0.7347
Epoch 2/2... Discriminator Loss: 1.3895... Generator Loss: 0.7302
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.7781
Epoch 2/2... Discriminator Loss: 1.3953... Generator Loss: 0.7257
Epoch 2/2... Discriminator Loss: 1.3770... Generator Loss: 0.8465
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.8098
Epoch 2/2... Discriminator Loss: 1.3924... Generator Loss: 0.6957
Epoch 2/2... Discriminator Loss: 1.3763... Generator Loss: 0.7649
Epoch 2/2... Discriminator Loss: 1.3802... Generator Loss: 0.7149
Epoch 2/2... Discriminator Loss: 1.4050... Generator Loss: 0.7534
Epoch 2/2... Discriminator Loss: 1.3814... Generator Loss: 0.7794
Epoch 2/2... Discriminator Loss: 1.3915... Generator Loss: 0.7115
Epoch 2/2... Discriminator Loss: 1.3883... Generator Loss: 0.7550
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.8408
Epoch 2/2... Discriminator Loss: 1.4016... Generator Loss: 0.9772
Epoch 2/2... Discriminator Loss: 1.3968... Generator Loss: 0.9401
Epoch 2/2... Discriminator Loss: 1.3861... Generator Loss: 0.7546
Epoch 2/2... Discriminator Loss: 1.3707... Generator Loss: 0.8623
Epoch 2/2... Discriminator Loss: 1.4205... Generator Loss: 0.6738
Epoch 2/2... Discriminator Loss: 1.3876... Generator Loss: 0.7532
Epoch 2/2... Discriminator Loss: 1.3835... Generator Loss: 0.7957
Epoch 2/2... Discriminator Loss: 1.4064... Generator Loss: 0.8782
Epoch 2/2... Discriminator Loss: 1.3897... Generator Loss: 0.8176
Epoch 2/2... Discriminator Loss: 1.3693... Generator Loss: 0.7534
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8041
Epoch 2/2... Discriminator Loss: 1.3867... Generator Loss: 0.7931
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.8218
Epoch 2/2... Discriminator Loss: 1.3968... Generator Loss: 0.8530
Epoch 2/2... Discriminator Loss: 1.3973... Generator Loss: 0.8017
Epoch 2/2... Discriminator Loss: 1.3857... Generator Loss: 0.8505
Epoch 2/2... Discriminator Loss: 1.4157... Generator Loss: 0.7034
Epoch 2/2... Discriminator Loss: 1.3938... Generator Loss: 0.7779
Epoch 2/2... Discriminator Loss: 1.3955... Generator Loss: 0.7915
Epoch 2/2... Discriminator Loss: 1.3982... Generator Loss: 0.7022
Epoch 2/2... Discriminator Loss: 1.3895... Generator Loss: 0.7577
Epoch 2/2... Discriminator Loss: 1.3954... Generator Loss: 0.7369
Epoch 2/2... Discriminator Loss: 1.3977... Generator Loss: 0.7065
Epoch 2/2... Discriminator Loss: 1.3822... Generator Loss: 0.7820
Epoch 2/2... Discriminator Loss: 1.3732... Generator Loss: 0.8238
Epoch 2/2... Discriminator Loss: 1.3789... Generator Loss: 0.7580
Epoch 2/2... Discriminator Loss: 1.3791... Generator Loss: 0.7581
Epoch 2/2... Discriminator Loss: 1.3846... Generator Loss: 0.8047
Epoch 2/2... Discriminator Loss: 1.3958... Generator Loss: 0.7261
Epoch 2/2... Discriminator Loss: 1.4025... Generator Loss: 0.6699
Epoch 2/2... Discriminator Loss: 1.3980... Generator Loss: 0.8093
Epoch 2/2... Discriminator Loss: 1.3907... Generator Loss: 0.8124
Epoch 2/2... Discriminator Loss: 1.3747... Generator Loss: 0.7790
Epoch 2/2... Discriminator Loss: 1.4048... Generator Loss: 0.9758
Epoch 2/2... Discriminator Loss: 1.3954... Generator Loss: 0.8238
Epoch 2/2... Discriminator Loss: 1.3955... Generator Loss: 0.8738
Epoch 2/2... Discriminator Loss: 1.4104... Generator Loss: 0.6705
Epoch 2/2... Discriminator Loss: 1.3857... Generator Loss: 0.8478
Epoch 2/2... Discriminator Loss: 1.3817... Generator Loss: 0.8022
Epoch 2/2... Discriminator Loss: 1.3881... Generator Loss: 0.9162
Epoch 2/2... Discriminator Loss: 1.3772... Generator Loss: 0.8520
Epoch 2/2... Discriminator Loss: 1.4032... Generator Loss: 0.7673
Epoch 2/2... Discriminator Loss: 1.4048... Generator Loss: 0.6919
Epoch 2/2... Discriminator Loss: 1.4067... Generator Loss: 0.8437
Epoch 2/2... Discriminator Loss: 1.3994... Generator Loss: 0.7337
Epoch 2/2... Discriminator Loss: 1.3943... Generator Loss: 0.8663
Epoch 2/2... Discriminator Loss: 1.3811... Generator Loss: 0.8628
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.7169
Epoch 2/2... Discriminator Loss: 1.3915... Generator Loss: 0.7884
Epoch 2/2... Discriminator Loss: 1.3831... Generator Loss: 0.8273
Epoch 2/2... Discriminator Loss: 1.3960... Generator Loss: 0.6975
Epoch 2/2... Discriminator Loss: 1.4127... Generator Loss: 0.6818
Epoch 2/2... Discriminator Loss: 1.3999... Generator Loss: 0.8291
Epoch 2/2... Discriminator Loss: 1.3805... Generator Loss: 0.7837
Epoch 2/2... Discriminator Loss: 1.3832... Generator Loss: 0.8484
Epoch 2/2... Discriminator Loss: 1.3887... Generator Loss: 0.8362
Epoch 2/2... Discriminator Loss: 1.3878... Generator Loss: 0.8224
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8780
Epoch 2/2... Discriminator Loss: 1.3938... Generator Loss: 0.6977
Epoch 2/2... Discriminator Loss: 1.3884... Generator Loss: 0.8742
Epoch 2/2... Discriminator Loss: 1.3790... Generator Loss: 0.8532
Epoch 2/2... Discriminator Loss: 1.3923... Generator Loss: 0.9156
Epoch 2/2... Discriminator Loss: 1.3718... Generator Loss: 0.7691
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.7800
Epoch 2/2... Discriminator Loss: 1.3744... Generator Loss: 0.8300
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.7843
Epoch 2/2... Discriminator Loss: 1.3907... Generator Loss: 0.8417
Epoch 2/2... Discriminator Loss: 1.3956... Generator Loss: 0.7798
Epoch 2/2... Discriminator Loss: 1.4001... Generator Loss: 0.7300
Epoch 2/2... Discriminator Loss: 1.3841... Generator Loss: 0.7154
Epoch 2/2... Discriminator Loss: 1.3836... Generator Loss: 0.7275
Epoch 2/2... Discriminator Loss: 1.3777... Generator Loss: 0.7511
Epoch 2/2... Discriminator Loss: 1.3927... Generator Loss: 0.7803
Epoch 2/2... Discriminator Loss: 1.3766... Generator Loss: 0.8005
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.8343
Epoch 2/2... Discriminator Loss: 1.4144... Generator Loss: 0.7060
Epoch 2/2... Discriminator Loss: 1.3981... Generator Loss: 0.9243
Epoch 2/2... Discriminator Loss: 1.3810... Generator Loss: 0.7290
Epoch 2/2... Discriminator Loss: 1.3791... Generator Loss: 0.7597
Epoch 2/2... Discriminator Loss: 1.3807... Generator Loss: 0.9004
Epoch 2/2... Discriminator Loss: 1.3995... Generator Loss: 0.8113
Epoch 2/2... Discriminator Loss: 1.3964... Generator Loss: 0.8861
Epoch 2/2... Discriminator Loss: 1.3890... Generator Loss: 0.7283
Epoch 2/2... Discriminator Loss: 1.4034... Generator Loss: 0.8704
Epoch 2/2... Discriminator Loss: 1.3755... Generator Loss: 0.7465
Epoch 2/2... Discriminator Loss: 1.3893... Generator Loss: 0.8759
Epoch 2/2... Discriminator Loss: 1.3791... Generator Loss: 0.7777
Epoch 2/2... Discriminator Loss: 1.3916... Generator Loss: 0.8480
Epoch 2/2... Discriminator Loss: 1.3816... Generator Loss: 0.8735
Epoch 2/2... Discriminator Loss: 1.3901... Generator Loss: 0.7775

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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